We can definitely say that supervised learning is challenging when dealing with imbalanced data. There are various ways to tackle this dilemma, such as generating synthetic data and modifying classification algorithms. Over-sampling techniques allow us to obtain additional data for training and improving efficacy, but they may introduce some fuzzy noise. Our research focuses on sensitivity costing technique, and we compare several algorithms that include cost balancing to effectively overcome the data imbalance across layers. We conducted extensive experiments with the dataset and found that applying the sensitivity cost technique improved classification results compared to the imbalanced classification dataset. Grid search class weights consistently outperformed other methods, producing better ROCAUC scores for XGBoost, LightGBM, and Keras neural network (Keras-NN).